Intestinal farnesoid X receptor (FXR) signaling is involved in the development of obesity, fatty liver disease, and type 2 diabetes. However, the role of intestinal FXR in atherosclerosis and its potential as a target for clinical treatment have not been explored. The serum levels of fibroblast growth factor 19 (FGF19), which is encoded by an FXR target gene, were much higher in patients with hypercholesterolemia than in control subjects and were positively related to circulating ceramide levels, indicating a link between intestinal FXR, ceramide metabolism, and atherosclerosis. Among ApoE–/– mice fed a high-cholesterol diet (HCD), intestinal FXR deficiency (in FxrΔIE ApoE–/– mice) or direct FXR inhibition (via treatment with the FXR antagonist glycoursodeoxycholic acid [GUDCA]) decreased atherosclerosis and reduced the levels of circulating ceramides and cholesterol. Sphingomyelin phosphodiesterase 3 (SMPD3), which is involved in ceramide synthesis in the intestine, was identified as an FXR target gene. SMPD3 overexpression or C16:0 ceramide supplementation eliminated the improvements in atherosclerosis in FxrΔIE ApoE–/– mice. Administration of GUDCA or GW4869, an SMPD3 inhibitor, elicited therapeutic effects on established atherosclerosis in ApoE–/– mice by decreasing circulating ceramide levels. This study identified an intestinal FXR/SMPD3 axis that is a potential target for atherosclerosis therapy.
Qing Wu, Lulu Sun, Xiaomin Hu, Xuemei Wang, Feng Xu, Bo Chen, Xianyi Liang, Jialin Xia, Pengcheng Wang, Daisuke Aibara, Shaofei Zhang, Guangyi Zeng, Chuyu Yun, Yu Yan, Yicheng Zhu, Michael Bustin, Shuyang Zhang, Frank J. Gonzalez, Changtao Jiang
Usage data is cumulative from May 2021 through May 2021.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.